Mapping dynamic QTL of stalk sugar content at different growth stages in maize

Bian, Yunlong; Gu, Xiao Hui; Sun, Daizhen; Wang, Yijun; Yin, Zhitong; Deng, Dexiang; Wang, Yanqiu; Li, Guosheng

doi:10.1007/s10681-015-1397-0

Cited by 12 publications

(12 citation statements)

References 29 publications

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“…QTLs for height at the vegetative stage are included in QTL regions that also contained QTL identified at flowering and/or harvest stages, emphasizing the relative stability of the height QTL. These results are similar to those reported in rice, wheat, maize, and switchgrass where the analysis of tillering and plant height QTL across different developmental stages also led to the identification of stage specific QTLs, as well as QTLs with different effects at different developmental stages [ 48 , 61 , 62 , 75 , 95 ].…”

Section: Discussionsupporting

confidence: 87%

“…The use of measurements at multiple time-points is advantageous as the timing and spatial location of branching events is distributed over the growth cycle of the plant. In fact, QTL analyses of tiller production and elongation in other species such as switchgrass, rice and wheat have identified different quantitative trait loci at different stages of the lifecycle [ 48 , 51 , 61 – 65 ]. As the Setaria system is phylogenetically close to potential biofuel grasses such as switchgrass, napier grass and pearl millet, understanding the relationships between the components of plant architecture across multiple developmental stages may aid in the breeding of better plant architectures for a variety of agricultural purposes including biomass and grain production.…”

Section: Introductionmentioning

confidence: 99%

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Development and Genetic Control of Plant Architecture and Biomass in the Panicoid Grass, Setaria

Mauro-Herrera

Doust

2016

PLoS ONE

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The architecture of a plant affects its ability to compete for light and to respond to environmental stresses, thus affecting overall fitness and productivity. Two components of architecture, branching and height, were studied in 182 F7 recombinant inbred lines (RILs) at the vegetative, flowering and mature developmental stages in the panicoid C4 model grass system, Setaria. The RIL population was derived from a cross between domesticated S. italica (foxtail millet) and its wild relative S. viridis (green foxtail). In both field and greenhouse trials the wild parent was taller initially, started branching earlier, and flowered earlier, while the domesticated parent was shorter initially, but flowered later, producing a robust tall plant architecture with more nodes and leaves on the main culm and few or no branches. Biomass was highly correlated with height of the plant and number of nodes on the main culm, and generally showed a negative relationship with branch number. However, several of the RILs with the highest biomass in both trials were significantly more branched than the domesticated parent of the cross. Quantitative trait loci (QTL) analyses indicate that both height and branching are controlled by multiple genetic regions, often with QTL for both traits colocalizing in the same genomic regions. Genomic positions of several QTL colocalize with QTL in syntenic regions in other species and contain genes known to control branching and height in sorghum, maize, and switchgrass. Included in these is the ortholog of the rice SD-1 semi-dwarfing gene, which underlies one of the major Setaria height QTL. Understanding the relationships between height and branching patterns in Setaria, and their genetic control, is an important step to gaining a comprehensive knowledge of the development and genetic regulation of panicoid grass architecture.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Development and Genetic Control of Plant Architecture and Biomass in the Panicoid Grass, Setaria

Mauro-Herrera

Doust

2016

PLoS ONE

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“…Sweet corn products like sweet corn milk and soups are gaining popularity in many countries, while kernels are sold as highly prized fresh-and canned-vegetables. Further, after harvest of the sweet corn cobs, green plants serve as source of fodder/forage to the cattle (Bian et al 2015), and therefore provide extra sources of income to farmers. Global import of frozen sweet corn was valued US $393 million, while the same for preserved sweet corn was estimated as US $968 million during 2011 (FAOSTAT 2014).…”

Section: Introductionmentioning

confidence: 99%

Microsatellite-based genetic diversity analyses of sugary1-, shrunken2- and double mutant- sweet corn inbreds for their utilization in breeding programme

Mehta

Hossain

Muthusamy

et al. 2017

Physiol Mol Biol Plants

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Sweet corn has recently experienced sharp rise in demand worldwide. Recessive sugary1 (su1) and shrunken2 (sh2) that enhances kernel sweetness have been abundantly used in sweet corn breeding. Analyses of genetic diversity among sweet corn inbreds assume great significance for their effective utilization in hybrid breeding. A set of 48 diverse sweet corn genotypes encompassing su1su1, sh2sh2 and su1su1/sh2sh2 types were analyzed using 56 microsatellite markers. A total of 213 alleles with mean of 3.8 alleles per locus were generated. Two unique-and 12 rare-alleles were identified. The average PIC and genetic dissimilarity was 0.50 and 0.73, respectively. Cluster analysis grouped the inbreds into three major clusters, with each of the su1su1-, sh2sh2-and su1su1/sh2sh2-types were broadly clustered together. Principal coordinate analyses also depicted the diverse origin of the genotypes. The study identified inbreds for synthesis of pools and pedigree populations to develop novel inbreds. The study led to the identification of prospective heterotic combinations in various genetic backgrounds (sh2sh2 9 sh2sh2, su1su1 9 su1-su1, su1su1/sh2sh2 9 su1su1/sh2sh2, sh2sh2 9 su1su1/ sh2sh2 and su1su1 9 su1su1/sh2sh2).

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“…Unconditional QTL mapping can be used to determine the results of accumulated genetic effects at measurement time t, thus providing information about "static" gene expression at time t. In contrast, conditional QTL mapping can reveal the gene expression levels from time t − 1 to time t, thereby reflecting the net effects of gene expression over a specific period and providing information about "dynamic" gene expression at time t (Bian et al 2015;Shang et al 2016;Zhang et al 2016). Combining unconditional and conditional QTL mapping results enables the detection of multiple QTLs, helps verify the results of the two methods, and simultaneously provides static and dynamic gene expression details (Jiang et al 2013).…”

Section: Combining Conditional and Unconditional Quantitative Trait Locimentioning

confidence: 99%

Quantitative trait loci for the diurnal flag leaf starch content during the early grain-filling stage in wheat (Triticum aestivum L.)

Zhao

Wang

Zhao

et al. 2018

Cereal Research Communications

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Starch is a product of photosynthetic activities in leaves. Wheat yields largely depend on photosynthetic carbon fixation and carbohydrate metabolism in flag leaves. The mapping of quantitative trait loci (QTLs) associated with flag leaf starch content (FLSC) in wheat (Triticum aestivum L.) was completed using unconditional and conditional QTL analyses. The FLSC of this population during the early grain-filling stage was measured at six stages in six environments. Combining unconditional and conditional QTL mapping methods, eight unconditional QTLs and nine conditional QTLs were detected, with five QTLs identified as unconditional and conditional QTLs. Four unconditional QTLs (i.e. qFLS-1B, qFLS-1D-1, qFLS-4A, and qFLS-7D-1) and one conditional QTL (i.e. qFLS-3A-1) were identified in two of six environments. Two QTLs (qFLS-1D-2 and qFLS-7D-1), which significantly affected the FLSC, were identified using the unconditional QTL mapping method, while three QTLs (i.e. qFLS-1A, qFLS-3A-1, and qFLS-7D-1) were detected using the conditional QTL mapping method. Our findings provide new insights into the genetic mechanism and regulatory network underlying the diurnal FLSC in wheat.

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Mapping dynamic QTL of stalk sugar content at different growth stages in maize

Cited by 12 publications

References 29 publications

Development and Genetic Control of Plant Architecture and Biomass in the Panicoid Grass, Setaria

Development and Genetic Control of Plant Architecture and Biomass in the Panicoid Grass, Setaria

Microsatellite-based genetic diversity analyses of sugary1-, shrunken2- and double mutant- sweet corn inbreds for their utilization in breeding programme

Quantitative trait loci for the diurnal flag leaf starch content during the early grain-filling stage in wheat (Triticum aestivum L.)

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